This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)
Apache License 2.0
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Reproduce the results PQ 61.12% on the Cityscapes datasets #115
Thanks for your works on Panoptic DeepLab. I am very newbie and tried to reproduce the results of panoptic segmentation on Cityscapes datasets with X65-DC5 backbone and the same configs. But I only got PQ (60.04%) which is lower.
Because of pool_kernel_size error, as in the comment, I uncommented the corresponding code in ASPP.py
Performance
PQ: 60.04
AP: 32.41
mIoU: 79.51
I could reproduce AP, mIoU results. I hope to know if this PQ results makes sense and is contained in expected range.
Moreover, I used two A100(80G) gpus and it requires total 140G for training 32 batch. Is it right memory size for training panoptic deeplab model?
Thanks for your works on Panoptic DeepLab. I am very newbie and tried to reproduce the results of panoptic segmentation on Cityscapes datasets with X65-DC5 backbone and the same configs. But I only got PQ (60.04%) which is lower.
Configs
Backbone = X65-DC5 (pretrained) Batch size = 32 learning rate = 0.001 Train_Iteration = 60k CROP SIZE = [512, 1024] Framework = detectron2
What I changed
Because of pool_kernel_size error, as in the comment, I uncommented the corresponding code in ASPP.py
Performance
PQ: 60.04 AP: 32.41 mIoU: 79.51
I could reproduce AP, mIoU results. I hope to know if this PQ results makes sense and is contained in expected range. Moreover, I used two A100(80G) gpus and it requires total 140G for training 32 batch. Is it right memory size for training panoptic deeplab model?